Bottom Line:
We also found that samples with very low starting amounts of RNA (mouse plasma) made high depth of mature miRNA coverage more difficult to obtain.We explored the changes in total mapped reads and differential expression results generated by three different software packages: miRDeep2, miRNAKey, and miRExpress and two different analysis packages, DESeq and EdgeR.We also examine the accuracy of using miRDeep2 to predict novel miRNAs and subsequently detect them in the samples using qRT-PCR.

ABSTRACTRecent advances in sample preparation and analysis for next generation sequencing have made it possible to profile and discover new miRNAs in a high throughput manner. In the case of neurological disease and injury, these types of experiments have been more limited. Possibly because tissues such as the brain and spinal cord are inaccessible for direct sampling in living patients, and indirect sampling of blood and cerebrospinal fluid are affected by low amounts of RNA. We used a mouse model to examine changes in miRNA expression in response to acute nerve crush. We assayed miRNA from both muscle tissue and blood plasma. We examined how the depth of coverage (the number of mapped reads) changed the number of detectable miRNAs in each sample type. We also found that samples with very low starting amounts of RNA (mouse plasma) made high depth of mature miRNA coverage more difficult to obtain. Each tissue must be assessed independently for the depth of coverage required to adequately power detection of differential expression, weighed against the cost of sequencing that sample to the adequate depth. We explored the changes in total mapped reads and differential expression results generated by three different software packages: miRDeep2, miRNAKey, and miRExpress and two different analysis packages, DESeq and EdgeR. We also examine the accuracy of using miRDeep2 to predict novel miRNAs and subsequently detect them in the samples using qRT-PCR.

Figure 2: Unique and overlapping miRNAs detected by each of the three programs for two sample types. (A) Results of miRKey, miRDeep2, miRExpress detection of miRNAs in the Gastrocnemius muscle samples. Venn diagram displays the overlapping and unique number of miRNAs detected by each program. (B) Results of miRKey, miRDeep2, miRExpress detection of miRNAs in the plasma samples. Venn diagram displays the overlapping and unique number of miRNAs detected by each program.

Mentions:
We examined the number of miRNAs that were detected by each software tool. Figure 2 displays the number of miRNAs detected in the mouse Gastrocnemius muscle and in the plasma samples altogether by miRExpress, miRNAKey, and miRDeep2. miRDeep2 software detected and aligned more miRNAs than miRExpress or miRNAKey; miRExpress and miRNAKey performed more similarly. In this analysis we counted every miRNA, even those that had only one detected read. We also adjusted the output from each tool so that there were not duplicate counts for any mature miRNAs.

Figure 2: Unique and overlapping miRNAs detected by each of the three programs for two sample types. (A) Results of miRKey, miRDeep2, miRExpress detection of miRNAs in the Gastrocnemius muscle samples. Venn diagram displays the overlapping and unique number of miRNAs detected by each program. (B) Results of miRKey, miRDeep2, miRExpress detection of miRNAs in the plasma samples. Venn diagram displays the overlapping and unique number of miRNAs detected by each program.

Mentions:
We examined the number of miRNAs that were detected by each software tool. Figure 2 displays the number of miRNAs detected in the mouse Gastrocnemius muscle and in the plasma samples altogether by miRExpress, miRNAKey, and miRDeep2. miRDeep2 software detected and aligned more miRNAs than miRExpress or miRNAKey; miRExpress and miRNAKey performed more similarly. In this analysis we counted every miRNA, even those that had only one detected read. We also adjusted the output from each tool so that there were not duplicate counts for any mature miRNAs.

Bottom Line:
We also found that samples with very low starting amounts of RNA (mouse plasma) made high depth of mature miRNA coverage more difficult to obtain.We explored the changes in total mapped reads and differential expression results generated by three different software packages: miRDeep2, miRNAKey, and miRExpress and two different analysis packages, DESeq and EdgeR.We also examine the accuracy of using miRDeep2 to predict novel miRNAs and subsequently detect them in the samples using qRT-PCR.

ABSTRACTRecent advances in sample preparation and analysis for next generation sequencing have made it possible to profile and discover new miRNAs in a high throughput manner. In the case of neurological disease and injury, these types of experiments have been more limited. Possibly because tissues such as the brain and spinal cord are inaccessible for direct sampling in living patients, and indirect sampling of blood and cerebrospinal fluid are affected by low amounts of RNA. We used a mouse model to examine changes in miRNA expression in response to acute nerve crush. We assayed miRNA from both muscle tissue and blood plasma. We examined how the depth of coverage (the number of mapped reads) changed the number of detectable miRNAs in each sample type. We also found that samples with very low starting amounts of RNA (mouse plasma) made high depth of mature miRNA coverage more difficult to obtain. Each tissue must be assessed independently for the depth of coverage required to adequately power detection of differential expression, weighed against the cost of sequencing that sample to the adequate depth. We explored the changes in total mapped reads and differential expression results generated by three different software packages: miRDeep2, miRNAKey, and miRExpress and two different analysis packages, DESeq and EdgeR. We also examine the accuracy of using miRDeep2 to predict novel miRNAs and subsequently detect them in the samples using qRT-PCR.